A major challenge of federated learning is performance when the distributions of the clients' data are heterogeneous, i.e. performance on “non-iid” data, as is often the circumstance in real-world applications of FL [1]}. FedAUX overcomes that challenge, performing remarkably more efficiently on non-iid data than other state-of-the-art federated learning methods, federated averaging, federated proximal learning, Bayesian federated learning, and federated ensemble distillation. For example on MobilenetV2, FedAUX achieves 64.8% server accuracy, while even the second-best method only achieves 46.7% [2]}.
